Parameter learning for relational Bayesian networks

We present a method for parameter learning in relational Bayesian networks (RBNs). Our approach consists of compiling the RBN model into a computation graph for the likelihood function, and to use this likelihood graph to perform the necessary computations for a gradient ascent likelihood optimization procedure. The method can be applied to all RBN models that only contain differentiable combining rules. This includes models with non-decomposable combining rules, as well as models with weighted combinations or nested occurrences of combining rules. Experimental results on artificial random graph data explores the feasibility of the approach both for complete and incomplete data.